Selective sensor polling

ABSTRACT

A selective sensor polling system for a voice activated data packet based computer network environment is provided. A system can receive audio signals detected by a microphone of a device. The system can parse the audio signal to identify trigger keyword and request. The system can select a template for an action data structure with a plurality of fields. The system can determine to poll a first sensor for data for the first field. The system can determine to obtain data in memory previously collected by the second sensor. The system can generate and transmit the action data structure with the data from the sensor and memory, and transmit the action data structure to a third party device.

BACKGROUND

Excessive network transmissions, packet-based or otherwise, of networktraffic data between computing devices can prevent a computing devicefrom properly processing the network traffic data, completing anoperation related to the network traffic data, or timely responding tothe network traffic data. The excessive network transmissions of networktraffic data can also complicate data routing or degrade the quality ofthe response if the responding computing device is at or above itsprocessing capacity, which may result in inefficient bandwidthutilization. The control of network transmissions corresponding tocontent item objects can be complicated by the large number of contentitem objects that can initiate network transmissions of network trafficdata between computing devices.

SUMMARY

The present disclosure is generally directed to selectively pollingsensors over a computer network. For example, computing systems may haveaccess to multiple sensors configured on multiple computing devices thatcan detect the same or similar types of information. However, it may beresource intensive to request the same or similar types of informationfrom multiple sensors either configured on the same computing device ora group of computing devices in close proximity to one another such thatthe detected information is similar. Furthermore, certain sensors, orcomputing device on which the sensor is configured, may consume greaterresources (e.g., energy, battery power, processor utilization, orbandwidth) as compared to other sensors or computing devices. Thus, thesystems and methods of the present disclosure can selectively poll oneor more sensors to obtain information in a manner that reduces resourceconsumption.

Systems and methods of the present disclosure are generally directed toa data processing system that selectively polls sensors over a computernetwork. The data processing system can process voice-based input usingvoice models trained on aggregated voice input and augmented with enduser voice input. The data processing system can identify a template foran action data structure based on a request and trigger keyword in thevoice input. The data processing system can determine to populate one ormore fields in the template to generate the action data structure. Topopulate the fields, the data processing system can interface with asensor management component to selectively poll sensors of one or morecomputing devices associated with the end user that provided the voiceinput. The sensor management component can apply a policy or set ofrules to identify one or more sensors that are available and can providethe values used to populate the fields of the template to generate theaction data structure. The data processing system can receive the valuesfrom the selected sensors, generate the action data structure, andtransmit the data structure to a third party provider device. The dataprocessing system can then receive an indication from the third partyprovider device that the operation corresponding to the action datastructure has been initiated.

At least one aspect is directed to a system to selectively poll sensors.The system can include a data processing system. The data processingsystem can execute a natural language processor (“NLP”) component,direct action application programming interface (“API”), and sensormanagement component. The NLP component can receive, via an interface ofthe data processing system, data packets comprising an input audiosignal detected by a microphone of a client device. The NLP can parsethe input audio signal to identify a request and a trigger keywordcorresponding to the request. The direct action API can select, based onthe trigger keyword, a template for an action data structure responsiveto the request. The template can include a first field. The sensormanagement component can identify a plurality of available sensorsconfigured to obtain information for the first field. The plurality ofavailable sensors can include a first sensor and a second sensor. Thesensor management component can determine a status of each of theplurality of sensors. The sensor management component can select thefirst sensor of the plurality of sensors based on the status. The sensormanagement component can poll first sensor for data corresponding to thefirst field. The direct action API can populate the first field with thedata received by the sensor management component responsive to the pollof the first sensor. The direct action API can generate the action datastructure based on the first field of the template. The direct actionAPI can transmit the action data structure to a third party providerdevice to cause the third party provider device to invoke an operationsession between the third party provider device and the client device.The data processing system can receive, from the third party providerdevice, an indication that the third party provider device establishedthe operation session with the client device.

At least one aspect is directed to a method of selectively pollingsensors. The method can include the data processing system receiving,via an interface, data packets comprising an input audio signal detectedby a microphone of a client device. The method can include the dataprocessing system parsing the input audio signal to identify a requestand a trigger keyword corresponding to the request. The method caninclude the data processing system selecting, based on the triggerkeyword, a template for an action data structure responsive to therequest. The template can include a first field. The method can includethe data processing system identifying a plurality of available sensorsconfigured to obtain information for the first field. The plurality ofavailable sensors can include a first sensor and a second sensor. Themethod can include the data processing system determining a status ofeach of the plurality of sensors. The method can include the dataprocessing system selecting the first sensor of the plurality of sensorsbased on the status. The method can include the data processing systempolling the first sensor for data corresponding to the first field. Themethod can include the data processing system populating the first fieldbased on the data received by the sensor management component responsiveto the poll of the first sensor. The method can include the dataprocessing system generating, by the direct action API, the action datastructure based on the first field of the template. The method caninclude the data processing system transmitting the action datastructure to a third party provider device. The method can include thedata processing system receiving, from the third party provider device,an indication that the third party provider device established theoperation session with the client device.

These and other aspects and implementations are discussed in detailbelow. The foregoing information and the following detailed descriptioninclude illustrative examples of various aspects and implementations,and provide an overview or framework for understanding the nature andcharacter of the claimed aspects and implementations. The drawingsprovide illustration and a further understanding of the various aspectsand implementations, and are incorporated in and constitute a part ofthis specification.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. Likereference numbers and designations in the various drawings indicate likeelements. For purposes of clarity, not every component may be labeled inevery drawing. In the drawings:

FIG. 1 is an illustration of a system to selectively poll sensors via acomputer network.

FIG. 2 is an illustration of an operation of system to selectively pollsensors via a computer network.

FIG. 3 is an illustration of a method of selectively polling sensorsover a computer network.

FIG. 4 is a block diagram illustrating a general architecture for acomputer system that can be employed to implement elements of thesystems and methods described and illustrated herein.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systems ofselectively polling sensors over a computer network. The variousconcepts introduced above and discussed in greater detail below may beimplemented in any of numerous ways.

The present disclosure is generally directed to selectively pollingsensors over a computer network. For example, computing systems may haveaccess to multiple sensors configured on multiple computing devices thatcan detect the same or similar types of information. However, it may beresource intensive to request the same or similar types of informationfrom multiple sensors either configured on the same computing device ora group of computing devices in close proximity to one another such thatthe detected information is similar. Furthermore, certain sensors, orcomputing device on which the sensor is configured, may consume greaterresources (e.g., energy, battery power, processor utilization, orbandwidth) as compared to other sensors or computing devices. Thus, thesystems and methods of the present disclosure can selectively poll oneor more sensors to obtain information in a manner that reduces resourceconsumption.

Systems and methods of the present disclosure are generally directed toa data processing system that selectively polls sensors over a computernetwork. The data processing system can process voice-based input usingvoice models trained on aggregated voice input and augmented with enduser voice input. The data processing system can identify a template foran action data structure based on a request and trigger keyword in thevoice input. The data processing system can determine to populate one ormore fields in the template to generate the action data structure. Topopulate the fields, the data processing system can interface with asensor management component to selectively poll sensors of one or morecomputing devices associated with the end user that provided the voiceinput. The sensor management component can apply a policy or set ofrules to identify one or more sensors that are available and can providethe values used to populate the fields of the template to generate theaction data structure. The data processing system can receive the valuesfrom the selected sensors, generate the action data structure, andtransmit the data structure to a third party provider device. The dataprocessing system can then receive an indication from the third partyprovider device that the operation corresponding to the action datastructure has been initiated.

For example, the data processing system can generally improve sensorselection and optimize sensor configurations for collecting data for useby a voice-based system. The data processing system can perform datasynchronization or a batch upload when a device enters an online statefrom an offline state. The data processing system can enable or disablesensors based on characteristics associated with the data beingcollected, the user, or the task. The data processing system canselectively enable sensors based on a policy that improves data qualitywhile reducing resource utilization. For example, the data processingsystem can determine that the user is carrying multiple devices withlocation sensors. The data processing system can determine which devicehas the greatest power remaining, and request the location from thatdevice only. In another example, the data processing system candetermine that a first device is already obtaining location information(e.g., via a navigational application) and piggy-back off that locationdata.

FIG. 1 illustrates an example system 100 to selectively poll sensors viaa computer network. The system 100 can include content selectioninfrastructure. The system 100 can include a data processing system 102.The data processing system 102 can communicate with one or more of acontent provider computing device 106, service provider computing device108, or client computing device 104 via a network 105. The network 105can include computer networks such as the Internet, local, wide, metro,or other area networks, intranets, satellite networks, and othercommunication networks such as voice or data mobile telephone networks.The network 105 can be used to access information resources such as webpages, web sites, domain names, or uniform resource locators that can bepresented, output, rendered, or displayed on at least one computingdevice 104, such as a laptop, desktop, tablet, personal digitalassistant, smart phone, portable computers, or speaker. For example, viathe network 105 a user of the computing device 104 can accessinformation or data provided by a service provider 108 or contentprovider 106.

The network 105 can include or constitute a display network, e.g., asubset of information resources available on the internet that areassociated with a content placement or search engine results system, orthat are eligible to include third party content items as part of acontent item placement campaign. The network 105 can be used by the dataprocessing system 102 to access information resources such as web pages,web sites, domain names, or uniform resource locators that can bepresented, output, rendered, or displayed by the client computing device104. For example, via the network 105 a user of the client computingdevice 104 can access information or data provided by the contentprovider computing device 106 or the service provider computing device108.

The network 105 may be any type or form of network and may include anyof the following: a point-to-point network, a broadcast network, a widearea network, a local area network, a telecommunications network, a datacommunication network, a computer network, an ATM (Asynchronous TransferMode) network, a SONET (Synchronous Optical Network) network, a SDH(Synchronous Digital Hierarchy) network, a wireless network and awireline network. The network 105 may include a wireless link, such asan infrared channel or satellite band. The topology of the network 105may include a bus, star, or ring network topology. The network mayinclude mobile telephone networks using any protocol or protocols usedto communicate among mobile devices, including advanced mobile phoneprotocol (“AMPS”), time division multiple access (“TDMA”), code-divisionmultiple access (“CDMA”), global system for mobile communication(“GSM”), general packet radio services (“GPRS”) or universal mobiletelecommunications system (“UMTS”). Different types of data may betransmitted via different protocols, or the same types of data may betransmitted via different protocols.

The system 100 can include at least one data processing system 102. Thedata processing system 102 can include at least one logic device such asa computing device having a processor to communicate via the network105, for example with the computing device 104, the content providerdevice 106 (content provider 106), or the service provider device 108(or service provider 108). The data processing system 102 can include atleast one computation resource, server, processor or memory. Forexample, the data processing system 102 can include a plurality ofcomputation resources or servers located in at least one data center.The data processing system 102 can include multiple, logically-groupedservers and facilitate distributed computing techniques. The logicalgroup of servers may be referred to as a data center, server farm or amachine farm. The servers can also be geographically dispersed. A datacenter or machine farm may be administered as a single entity, or themachine farm can include a plurality of machine farms. The serverswithin each machine farm can be heterogeneous—one or more of the serversor machines can operate according to one or more type of operatingsystem platform.

Servers in the machine farm can be stored in high-density rack systems,along with associated storage systems, and located in an enterprise datacenter. For example, consolidating the servers in this way may improvesystem manageability, data security, the physical security of thesystem, and system performance by locating servers and high performancestorage systems on localized high performance networks. Centralizationof all or some of the data processing system 102 components, includingservers and storage systems, and coupling them with advanced systemmanagement tools allows more efficient use of server resources, whichsaves power and processing requirements and reduces bandwidth usage.

The system 100 can include, access, or otherwise interact with at leastone service provider device 108. The service provider device 108 caninclude at least one logic device such as a computing device having aprocessor to communicate via the network 105, for example with thecomputing device 104, the data processing system 102, or the contentprovider 106. The service provider device 108 can include at least onecomputation resource, server, processor or memory. For example, serviceprovider device 108 can include a plurality of computation resources orservers located in at least one data center. The service provider device108 can include one or more component or functionality of the dataprocessing system 102.

The content provider computing device 106 can provide audio basedcontent items for display by the client computing device 104 as an audiooutput content item. The content item can include an offer for a good orservice, such as a voice based message that states: “Would you like meto order you a taxi?” For example, the content provider computing device155 can include memory to store a series of audio content items that canbe provided in response to a voice based query. The content providercomputing device 106 can also provide audio based content items (orother content items) to the data processing system 102 where they can bestored in the data repository 124. The data processing system 102 canselect the audio content items and provide (or instruct the contentprovider computing device 104 to provide) the audio content items to theclient computing device 104. The audio based content items can beexclusively audio or can be combined with text, image, or video data.

The service provider device 108 can include, interface, or otherwisecommunicate with at least one service provider natural languageprocessor component 142 and a service provider interface 144. Theservice provider computing device 108 can include at least one serviceprovider natural language processor (NLP) component 142 and at least oneservice provider interface 144. The service provider NLP component 142(or other components such as a direct action API of the service providercomputing device 108) can engage with the client computing device 104(via the data processing system 102 or bypassing the data processingsystem 102) to create a back-and-forth real-time voice or audio basedconversation (e.g., a session) between the client computing device 104and the service provider computing device 108. The service provider NLP142 can include one or more function or feature as the NLP component 112of the data processing system 102. For example, the service providerinterface 144 can receive or provide data messages to the direct actionAPI 116 of the data processing system 102. The service providercomputing device 108 and the content provider computing device 106 canbe associated with the same entity. For example, the content providercomputing device 106 can create, store, or make available content itemsfor a car sharing service, and the service provider computing device 108can establish a session with the client computing device 106 to arrangefor a delivery of a taxi or car of the car share service to pick up theend user of the client computing device 104. The data processing system102, via the direct action API 116, the NLP component 112 or othercomponents can also establish the session with the client computingdevice, including or bypassing the service provider computing device104, to arrange for example for a delivery of a taxi or car of the carshare service.

The computing device 104 can include, interface, or otherwisecommunicate with at least one sensor 134, transducer 136, audio driver138, or pre-processor 140. The sensor 134 can include, for example, anambient light sensor, proximity sensor, temperature sensor,accelerometer, gyroscope, motion detector, GPS sensor, location sensor,microphone, or touch sensor. The transducer 136 can include a speaker ora microphone. The audio driver 138 can provide a software interface tothe hardware transducer 136. The audio driver can execute the audio fileor other instructions provided by the data processing system 102 tocontrol the transducer 136 to generate a corresponding acoustic wave orsound wave. The pre-processor 140 can be configured to detect a keywordand perform an action based on the keyword. The pre-processor 140 canfilter out one or more terms or modify the terms prior to transmittingthe terms to the data processing system 102 for further processing. Thepre-processor 140 can convert the analog audio signals detected by themicrophone into a digital audio signal, and transmit one or more datapackets carrying the digital audio signal to the data processing system102 via the network 105. In some cases, the pre-processor 140 cantransmit data packets carrying some or all of the input audio signalresponsive to detecting an instruction to perform such transmission. Theinstruction can include, for example, a trigger keyword or other keywordor approval to transmit data packets comprising the input audio signalto the data processing system 102. The computing device 104 may or maynot include a display; for example, the computing device may includelimited types of user interfaces, such as a microphone and speaker. Insome cases, the primary user interface of the computing device 104 maybe a microphone and speaker.

The client computing device 104 can be associated with an end user thatenters voice queries as audio input into the client computing device 104(via the sensor 134) and receives audio output in the form of a computergenerated voice that can be provided from the data processing system 102(or the content provider computing device 106 or the service providercomputing device 108) to the client computing device 104, output fromthe transducer 136 (e.g., a speaker). The computer generated voice caninclude recordings from a real person or computer generated language.

The data repository 124 can include one or more local or distributeddatabases, and can include a database management system. The datarepository 124 can include computer data storage or memory and can storeone or more parameters 126, one or more policies 128, content data 130,or templates 132 among other data. The parameters 126, policies 128, andtemplates 132 can include information such as rules about a voice basedsession between the client computing device 104 and the data processingsystem 102 (or the service provider computing device 108). The contentdata 130 can include content items for audio output or associatedmetadata, as well as input audio messages that can be part of one ormore communication sessions with the client computing device 104. Thecommunication session can include or be referred to as an operationsession. In some cases, an operation session can refer to acommunication session in which one or more operations are performed bythe third party provider device 108, client computing device, contentprovider device 106, data processing system 102 or other component orentity.

The data processing system 102 can include a content placement systemhaving at least one computation resource or server. The data processingsystem 102 can include, interface, or otherwise communicate with atleast one interface 110. The data processing system 102 can include,interface, or otherwise communicate with at least one natural languageprocessor component 112. The data processing system 102 can include,interface, or otherwise communicate with at least one direct actionapplication programming interface (“API”) 116. The data processingsystem 102 can include, interface, or otherwise communicate with atleast one session handler 114. The data processing system 102 caninclude, interface, or otherwise communicate with at least one contentselector component 118. The data processing system 102 can include,interface, or otherwise communicate with at least one sensor managementcomponent 120. The data processing system 102 can include, interface, orotherwise communicate with at least one audio signal generator 122. Thedata processing system 102 can include, interface, or otherwisecommunicate with at least one data repository 124. The at least one datarepository 124 can include or store, in one or more data structures ordatabases, parameters 126, policies 128, content data 130, or templates132. Parameters 126 can include, for example, thresholds, distances,time intervals, durations, scores, or weights. Content data 130 caninclude, for example, content campaign information, content groups,content selection criteria, content item objects or other informationprovided by a content provider 106 or obtained or determined by the dataprocessing system to facilitate content selection. The content data 130can include, for example, historical performance of a content campaign.

The interface 110, natural language processor component 112, sessionhandler 114, direct action API 116, content selector component 118,sensor management component 120, or audio signal generator component 122can each include at least one processing unit or other logic device suchas programmable logic array engine, or module configured to communicatewith the database repository or database 124. The interface 110, naturallanguage processor component 112, session handler 114, direct action API116, content selector component 118, sensor management component 120,audio signal generator component 122 and data repository 124 can beseparate components, a single component, or part of the data processingsystem 102. The system 100 and its components, such as a data processingsystem 102, can include hardware elements, such as one or moreprocessors, logic devices, or circuits.

The data processing system 102 can obtain anonymous computer networkactivity information associated with a plurality of computing devices104. A user of a computing device 104 can affirmatively authorize thedata processing system 102 to obtain network activity informationcorresponding to the user's computing device 104. For example, the dataprocessing system 102 can prompt the user of the computing device 104for consent to obtain one or more types of network activity information.The identity of the user of the computing device 104 can remainanonymous and the computing device 104 can be associated with a uniqueidentifier (e.g., a unique identifier for the user or the computingdevice provided by the data processing system or a user of the computingdevice). The data processing system can associate each observation witha corresponding unique identifier.

A content provider 106 can establish an electronic content campaign. Theelectronic content campaign can be stored as content data 130 in datarepository 124. An electronic content campaign can refer to one or morecontent groups that correspond to a common theme. A content campaign caninclude a hierarchical data structure that includes content groups,content item data objects, and content selection criteria. To create acontent campaign, content provider 106 can specify values for campaignlevel parameters of the content campaign. The campaign level parameterscan include, for example, a campaign name, a preferred content networkfor placing content item objects, a value of resources to be used forthe content campaign, start and end dates for the content campaign, aduration for the content campaign, a schedule for content item objectplacements, language, geographical locations, type of computing deviceson which to provide content item objects. In some cases, an impressioncan refer to when a content item object is fetched from its source(e.g., data processing system 102 or content provider 106), and iscountable. In some cases, due to the possibility of click fraud, roboticactivity can be filtered and excluded, as an impression. Thus, in somecases, an impression can refer to a measurement of responses from a Webserver to a page request from a browser, which is filtered from roboticactivity and error codes, and is recorded at a point as close aspossible to opportunity to render the content item object for display onthe computing device 104. In some cases, an impression can refer to aviewable or audible impression; e.g., the content item object is atleast partially (e.g., 20%, 30%, 30%, 40%, 50%, 60%, 70%, or more)viewable on a display device of the client computing device 104, oraudible via a speaker 136 of the computing device 104. A click orselection can refer to a user interaction with the content item object,such as a voice response to an audible impression, a mouse-click, touchinteraction, gesture, shake, audio interaction, or keyboard click. Aconversion can refer to a user taking a desired action with respect tothe content item objection; e.g., purchasing a product or service,completing a survey, visiting a physical store corresponding to thecontent item, or completing an electronic transaction.

The content provider 106 can further establish one or more contentgroups for a content campaign. A content group includes one or morecontent item objects and corresponding content selection criteria, suchas keywords, words, terms, phrases, geographic locations, type ofcomputing device, time of day, interest, topic, or vertical. Contentgroups under the same content campaign can share the same campaign levelparameters, but may have tailored specifications for particular contentgroup level parameters, such as keywords, negative keywords (e.g., thatblock placement of the content item in the presence of the negativekeyword on main content), bids for keywords, or parameters associatedwith the bid or content campaign.

To create a new content group, the content provider can provide valuesfor the content group level parameters of the content group. The contentgroup level parameters include, for example, a content group name orcontent group theme, and bids for different content placementopportunities (e.g., automatic placement or managed placement) oroutcomes (e.g., clicks, impressions, or conversions). A content groupname or content group theme can be one or more terms that the contentprovider 106 can use to capture a topic or subject matter for whichcontent item objects of the content group is to be selected for display.For example, a car dealership can create a different content group foreach brand of vehicle it carries, and may further create a differentcontent group for each model of vehicle it carries. Examples of thecontent group themes that the car dealership can use include, forexample, “Make A sports car” “Make B sports car,” “Make C sedan,” “MakeC truck,” “Make C hybrid,” or “Make D hybrid.” An example contentcampaign theme can be “hybrid” and include content groups for both “MakeC hybrid” and “Make D hybrid”, for example.

The content provider 106 can provide one or more keywords and contentitem objects to each content group. Keywords can include terms that arerelevant to the product or services of associated with or identified bythe content item objects. A keyword can include one or more terms orphrases. For example, the car dealership can include “sports car,” “V-6engine,” “four-wheel drive,” “fuel efficiency,” as keywords for acontent group or content campaign. In some cases, negative keywords canbe specified by the content provider to avoid, prevent, block, ordisable content placement on certain terms or keywords. The contentprovider can specify a type of matching, such as exact match, phrasematch, or broad match, used to select content item objects.

The content provider 106 can provide one or more keywords to be used bythe data processing system 102 to select a content item object providedby the content provider 106. The content provider 106 can identify oneor more keywords to bid on, and further provide bid amounts for variouskeywords. The content provider 106 can provide additional contentselection criteria to be used by the data processing system 102 toselect content item objects. Multiple content providers 106 can bid onthe same or different keywords, and the data processing system 102 canrun a content selection process or ad auction responsive to receiving anindication of a keyword of an electronic message.

The content provider 106 can provide one or more content item objectsfor selection by the data processing system 102. The data processingsystem 102 (e.g., via content selector component 118) can select thecontent item objects when a content placement opportunity becomesavailable that matches the resource allocation, content schedule,maximum bids, keywords, and other selection criteria specified for thecontent group. Different types of content item objects can be includedin a content group, such as a voice content item, audio content item, atext content item, an image content item, video content item, multimediacontent item, or content item link. Upon selecting a content item, thedata processing system 102 can transmit the content item object forrendering on a computing device 104 or display device of the computingdevice 104. Rendering can include displaying the content item on adisplay device, or playing the content item via a speaker of thecomputing device 104. The data processing system 102 can provideinstructions to a computing device 104 to render the content itemobject. The data processing system 102 can instruct the computing device104, or an audio driver 138 of the computing device 104, to generateaudio signals or acoustic waves.

The data processing system 102 can include an interface component 110designed, configured, constructed, or operational to receive andtransmit information using, for example, data packets. The interface 110can receive and transmit information using one or more protocols, suchas a network protocol. The interface 110 can include a hardwareinterface, software interface, wired interface, or wireless interface.The interface 110 can facilitate translating or formatting data from oneformat to another format. For example, the interface 110 can include anapplication programming interface that includes definitions forcommunicating between various components, such as software components.

The data processing system 102 can include an application, script orprogram installed at the client computing device 104, such as an app tocommunicate input audio signals to the interface 110 of the dataprocessing system 102 and to drive components of the client computingdevice to render output audio signals. The data processing system 102can receive data packets or other signal that includes or identifies anaudio input signal. For example, the data processing system 102 canexecute or run the NLP component 112 to receive or obtain the audiosignal and parse the audio signal. For example, the NLP component 112can provide for interactions between a human and a computer. The NLPcomponent 112 can be configured with techniques for understandingnatural language and allowing the data processing system 102 to derivemeaning from human or natural language input. The NLP component 112 caninclude or be configured with technique based on machine learning, suchas statistical machine learning. The NLP component 112 can utilizedecision trees, statistical models, or probabilistic models to parse theinput audio signal. The NLP component 112 can perform, for example,functions such as named entity recognition (e.g., given a stream oftext, determine which items in the text map to proper names, such aspeople or places, and what the type of each such name is, such asperson, location, or organization), natural language generation (e.g.,convert information from computer databases or semantic intents intounderstandable human language), natural language understanding (e.g.,convert text into more formal representations such as first-order logicstructures that a computer module can manipulate), machine translation(e.g., automatically translate text from one human language to another),morphological segmentation (e.g., separating words into individualmorphemes and identify the class of the morphemes, which can bechallenging based on the complexity of the morphology or structure ofthe words of the language being considered), question answering (e.g.,determining an answer to a human-language question, which can bespecific or open-ended), semantic processing (e.g., processing that canoccur after identifying a word and encoding its meaning in order torelate the identified word to other words with similar meanings).

The NLP component 112 converts the audio input signal into recognizedtext by comparing the input signal against a stored, representative setof audio waveforms (e.g., in the data repository 124) and choosing theclosest matches. The set of audio waveforms can be stored in datarepository 124 or other database accessible to the data processingsystem 102. The representative waveforms are generated across a largeset of users, and then may be augmented with speech samples from theuser. After the audio signal is converted into recognized text, the NLPcomponent 112 matches the text to words that are associated, for examplevia training across users or through manual specification, with actionsthat the data processing system 102 can serve.

The audio input signal can be detected by the sensor 134 or transducer136 (e.g., a microphone) of the client computing device 104. Via thetransducer 136, the audio driver 138, or other components the clientcomputing device 104 can provide the audio input signal to the dataprocessing system 102 (e.g., via the network 105) where it can bereceived (e.g., by the interface 110) and provided to the NLP component112 or stored in the data repository 124.

The NLP component 112 can obtain the input audio signal. From the inputaudio signal, the NLP component 112 can identify at least one request orat least one trigger keyword corresponding to the request. The requestcan indicate intent or subject matter of the input audio signal. Thetrigger keyword can indicate a type of action likely to be taken. Forexample, the NLP component 112 can parse the input audio signal toidentify at least one request to leave home for the evening to attenddinner and a movie. The trigger keyword can include at least one word,phrase, root or partial word, or derivative indicating an action to betaken. For example, the trigger keyword “go” or “to go to” from theinput audio signal can indicate a need for transport. In this example,the input audio signal (or the identified request) does not directlyexpress an intent for transport, however the trigger keyword indicatesthat transport is an ancillary action to at least one other action thatis indicated by the request.

The NLP component 112 can parse the input audio signal to identify,determine, retrieve, or otherwise obtain the request and the triggerkeyword. For instance, the NLP component 112 can apply a semanticprocessing technique to the input audio signal to identify the triggerkeyword or the request. The NLP component 112 can apply the semanticprocessing technique to the input audio signal to identify a triggerphrase that includes one or more trigger keywords, such as a firsttrigger keyword and a second trigger keyword. For example, the inputaudio signal can include the sentence “I need someone to do my laundryand my dry cleaning.” The NLP component 112 can apply a semanticprocessing technique, or other natural language processing technique, tothe data packets comprising the sentence to identify trigger phrases “domy laundry” and “do my dry cleaning”. The NLP component 112 can furtheridentify multiple trigger keywords, such as laundry, and dry cleaning.For example, the NLP component 112 can determine that the trigger phraseincludes the trigger keyword and a second trigger keyword.

The NLP component 112 can filter the input audio signal to identify thetrigger keyword. For example, the data packets carrying the input audiosignal can include “It would be great if I could get someone that couldhelp me go to the airport”, in which case the NLP component 112 canfilter out one or more terms as follows: “it”, “would”, “be”, “great”,“if”, “I”, “could”, “get”, “someone”, “that”, “could”, or “help”. Byfiltering out these terms, the NLP component 112 may more accurately andreliably identify the trigger keywords, such as “go to the airport” anddetermine that this is a request for a taxi or a ride sharing service.

In some cases, the NLP component can determine that the data packetscarrying the input audio signal includes one or more requests. Forexample, the input audio signal can include the sentence “I need someoneto do my laundry and my dry cleaning.” The NLP component 112 candetermine this is a request for a laundry service and a dry cleaningservice. The NLP component 112 can determine this is a single requestfor a service provider that can provide both laundry services and drycleaning services. The NLP component 112 can determine that this is tworequests; a first request for a service provider that performs laundryservices, and a second request for a service provider that provides drycleaning services. In some cases, the NLP component 112 can combine themultiple determined requests into a single request, and transmit thesingle request to a service provider device 108. In some cases, the NLPcomponent 112 can transmit the individual requests to respective serviceprovider devices 108, or separately transmit both requests to the sameservice provider device 108.

The data processing system 102 can include a direct action API 116designed and constructed to generate, based on the trigger keyword, anaction data structure responsive to the request. Processors of the dataprocessing system 102 can invoke the direct action API 116 to executescripts that generate a data structure to a service provider device 108to request or order a service or product, such as a car from a car shareservice. The direct action API 116 can obtain data from the datarepository 124, as well as data received with end user consent from theclient computing device 104 to determine location, time, user accounts,logistical or other information to allow the service provider device 108to perform an operation, such as reserve a car from the car shareservice. Using the direct action API 116, the data processing system 102can also communicate with the service provider device 108 to completethe conversion by in this example making the car share pick upreservation.

The direct action API 116 can execute a specified action to satisfy theend user's intention, as determined by the data processing system 102.Depending on the action specified in its inputs, the direct action API116 can execute code or a dialog script that identifies the parametersrequired to fulfill a user request. Such code can look-up additionalinformation, e.g., in the data repository 124, such as the name of ahome automation service, or it can provide audio output for rendering atthe client computing device 104 to ask the end user questions such asthe intended destination of a requested taxi. The direct action API 116can determine necessary parameters and can package the information intoan action data structure, which can then be sent to another componentsuch as the content selector component 118 or to the service providercomputing device 108 to be fulfilled.

The direct action API 116 can receive an instruction or command from theNLP component 112, or other component of the data processing system 102,to generate or construct the action data structure. The direct actionAPI 116 can determine a type of action in order to select a templatefrom the template repository 132 stored in the data repository 124.Types of actions can include, for example, services, products,reservations, or tickets. Types of actions can further include types ofservices or products. For example, types of services can include carshare service, food delivery service, laundry service, maid service,repair services, or household services. Types of products can include,for example, clothes, shoes, toys, electronics, computers, books, orjewelry. Types of reservations can include, for example, dinnerreservations or hair salon appointments. Types of tickets can include,for example, movie tickets, sports venue tickets, or flight tickets. Insome cases, the types of services, products, reservations or tickets canbe categorized based on price, location, type of shipping, availability,or other attributes.

The direct action API 116, upon identifying the type of request, canaccess the corresponding template from the template repository 132.Templates can include fields in a structured data set that can bepopulated by the direct action API 116 to further the operation that isrequested of the service provider device 108 (such as the operation ofsending a taxi to pick up an end user at a pickup location and transportthe end user to a destination location). The direct action API 116 canperform a lookup in the template repository 132 to select the templatethat matches one or more characteristic of the trigger keyword andrequest. For example, if the request corresponds to a request for a caror ride to a destination, the data processing system 102 can select acar sharing service template. The car sharing service template caninclude one or more of the following fields: device identifier, pick uplocation, destination location, number of passengers, or type ofservice. The direct action API 116 can populate the fields with values.To populate the fields with values, the direct action API 116 can ping,poll or otherwise obtain information from one or more sensors 134 of thecomputing device 104 or a user interface of the device 104.

Polling can refer to actively sampling information of an externaldevice, such as sensor 134, by the data processing system 102, or by thecomputing device 104 responsive to an instruction from the dataprocessing system 102. Polling can be a synchronous activity. Duringpolling, the data processing system 102 can wait for the sensor 134 tocheck its readiness, state, detect an environmental condition, orperform any other function or activity that the sensor 134 is configuredto perform (e.g., collect and record a temperature reading; detectambient light level; determine a location; determine pressure; determinealtitude; determine speed of motion; or determine direction of motion).In some cases, polling can refer to requesting data from the computingdevice 104 that is collected, measured, detected or otherwise determinedat least in part by using one or more sensors 134.

For example, the direct action API 116 can detect the source locationusing a location sensor, such as a GPS sensor. The direct action API 116can obtain further information by submitting a survey, prompt, or queryto the end of user of the computing device 104. The direct action APIcan submit the survey, prompt, or query via interface 110 of the dataprocessing system 102 and a user interface of the computing device 104(e.g., audio interface, voice-based user interface, display, or touchscreen). Thus, the direct action API 116 can select a template for theaction data structure based on the trigger keyword or the request,populate one or more fields in the template with information detected byone or more sensors 134 or obtained via a user interface, and generate,create or otherwise construct the action data structure to facilitateperformance of an operation by the service provider device 108.

The data processing system 102 can select the template based from thetemplate data structure 132 based on various factors including, forexample, one or more of the trigger keyword, request, third partyprovider device 108, type of third party provider device 108, a categorythat the third party provider device 108 falls in (e.g., taxi service,laundry service, flower service, or food delivery), location, or othersensor information.

To select the template based on the trigger keyword, the data processingsystem 102 (e.g., via direct action API 116) can perform a look-up orother query operation on the template database 132 using the triggerkeyword to identify a template data structure that maps or otherwisecorresponds to the trigger keyword. For example, each template in thetemplate database 132 can be associated with one or more triggerkeywords to indicate that the template is configured to generate anaction data structure responsive to the trigger keyword that the thirdparty provider device 108 can process to establish a communicationsession.

In some cases, the data processing system 102 can identify a third partyprovider device 108 based on the trigger keyword. To identify the thirdparty provide 108 based on the trigger keyword, the data processingsystem 102 can perform a lookup in the data repository 124 to identify athird party provider device 108 that maps to the trigger keyword. Forexample, if the trigger keyword includes “ride” or “to go to”, then thedata processing system 102 (e.g., via direct action API 116) canidentify the third party provider device 108 as corresponding to TaxiService Company A. The data processing system 102 can select thetemplate from the template database 132 using the identify third partyprovider device 108. For example, the template database 132 can includea mapping or correlation between third party provider devices 108 orentities to templates configured to generate an action data structureresponsive to the trigger keyword that the third party provider device108 can process to establish a communication session. In some cases, thetemplate can be customized for the third party provider device 108 orfor a category of third party provider devices 108. The data processingsystem 102 can generate the action data structure based on the templatefor the third party provider 108.

To construct or generate the action data structure, the data processingsystem 102 can identify one or more fields in the selected template topopulate with values. The fields can be populated with numerical values,character strings, Unicode values, Boolean logic, binary values,hexadecimal values, identifiers, location coordinates, geographic areas,timestamps, or other values. The fields or the data structure itself canbe encrypted or masked to maintain data security.

Upon determining the fields in the template, the data processing system102 can identify the values for the fields to populate the fields of thetemplate to create the action data structure. The data processing system102 can obtain, retrieve, determine or otherwise identify the values forthe fields by performing a look-up or other query operation on the datarepository 124.

In some cases, the data processing system 102 can determine that theinformation or values for the fields are absent from the data repository124. The data processing system 102 can determine that the informationor values stored in the data repository 124 are out-of-date, stale, orotherwise not suitable for the purpose of constructing the action datastructure responsive to the trigger keyword and request identified bythe NLP component 112 (e.g., the location of the client computing device104 may be the old location and not be the current location; an accountcan be expired; the destination restaurant may have moved to a newlocation; physical activity information; or mode of transportation).

If the data processing system 102 determines that it does not currentlyhave access, in memory of the data processing system 102, to the valuesor information for the field of the template, the data processing system102 can acquire the values or information. The data processing system102 can acquire or obtain the information by querying or polling one ormore available sensors of the client computing device 104, prompting theend user of the client computing device 104 for the information, oraccessing an online web-based resource using an HTTP protocol. Forexample, the data processing system 102 can determine that it does nothave the current location of the client computing device 104, which maybe a needed field of the template. The data processing system 102 canquery the client computing device 104 for the location information. Thedata processing system 102 can request the client computing device 104to provide the location information using one or more location sensors134, such as a Global Positioning System sensor, WIFI triangulation,cell tower triangulation, Bluetooth beacons, IP address, or otherlocation sensing technique.

In some cases, the direct action API 116 can request sensor informationfrom a sensor management component 120. The data processing system 102can include a sensor management component 120. The sensor managementcomponent 120 can execute on the data processing system 102 separate orindependent from the client computing device 104. In some cases, thesensor management component 120 can include one or more agents, scripts,executables that are configured on the computing device 104 to interfacewith the sensor management component 120 executing on the dataprocessing system 102.

The sensor management component 120 can include hardware or software tomeasure the characteristic of the communication session. For example,the direct action API 116 can select, based on the trigger keyword, atemplate for an action data structure that corresponds to or isresponsive to the request. The template can include one or more fieldsto be populated, such as a first field and a second field. To populatethese fields, the direct action API 116 can query, request from, invoke,or otherwise interface with the sensor management component 120. Thesensor management component 120 can obtain the information for thefields by reducing resource utilization by sensors 134 or computingdevices 104 associated with the sensors 134. For example, the sensormanagement component 120 can identify a plurality of available sensors134 configured to obtain information for the first field. The pluralityof available sensors can include a first sensor and a second sensor. Thesensor management component 120 can determine a status of each of theplurality of sensors. The sensor management component 120 can select thefirst sensor of the plurality of sensors based on the status. The sensormanagement component 120 can poll first sensor for data corresponding tothe first field. The direct action API 116 can populate the first fieldwith the data received by the sensor management component responsive tothe poll of the first sensor.

The sensor management component 120 can identify a plurality ofavailable sensors 134. The sensor management component 120 can store, indata repository 124, a list of sensors 134 that are available for an enduser account associated with a computing device 104. The sensors 134 mayhave a status as available. Available can refer to the sensor beingonline, active, standby mode, or in low power mode. A sensor can beavailable if it passes a diagnostic test or process. In some cases, asensor 134 may be available even if it is offline if the computingdevice 104 can be instructed to bring the sensor 134 online. The statusof the sensor 134 may be unavailable if the sensor 134 is offline, doesnot respond timely to a ping, is malfunctioning, provides erroneous orinconsistent values, or fails a diagnostic test or process.

In some cases, the client computing device 104 can push sensor statusinformation to the sensor management component 120 upon establishing acommunication session. In some cases, the sensor management component120 may send a request to the client computing device 104 or one or moreclient computing devices 104 associated with the account identifier fora list of available sensors 134. The sensor management component 120 canidentify a set of one or more computing devices 104 based on a proximityof the computing device to one another, or based on network activityassociated with the one or more computing devices 104 (e.g., the enduser may be actively using the one or more computing devices 104 tointeract with the data processing system 102 or component thereof). Insome cases, the data processing system 102 can poll all previously knownsensors 134 used by the sensor management component based on a timeinterval.

Thus, data processing system 102 can identify multiple sensors capableof providing, and available to provide, the data to populate the firstfield of the template. The data processing system can then select one(or a subset) of the sensors from which to receive data. The dataprocessing system 102 can determine to poll a sensor that is not coupledto the computing device 104 that provided the input audio signal. Forexample, an end user can use a first computing device 104 to provide theinput audio signal. The first computing device 104 can include a firstsensor 134 coupled to the first computing device 104. The dataprocessing system can determine that there is a second computing device104 that is also associated with the end user account (e.g., both thefirst and second computing devices 104 successfully performed ahandshaking process with the data processing system 102). The secondcomputing device 104 can be proximate to the first computing device 104,or the data processing system 102 can otherwise determine that thesecond computing device 104 can also provide the data of sufficientquality to populate the first field of the template (e.g., the locationinformation that would be provided by the two sensors is within atolerance level, such as 25 meters, 50 meters, 75 meters, or 100meters). The data processing system 102 can determine to use the secondsensor 134 that is coupled to the second computing device 104 instead ofthe first sensor 134 coupled to the first computing device, even thoughthe first computing 104 device invoked, initiated, or established thecommunication with the data processing system 102. The data processingsystem 102 can use a resource reduction policy to determine to use thesecond sensor 134 coupled to the second computing device instead of thefirst sensor 134 coupled to the first computing device 104 that providedthe input audio signal to the NLP component 110. The data processingsystem 102 can determine to use second computing device 104 because itmay have more battery remaining, have greater resource availability, orbe configured to provide higher quality or more accurate data that canresult in fewer subsequent requests for sensor information.

In some cases, the sensor management component 120 can reduce resourceutilization by having a first sensor detect a first environmentalcondition (e.g., location, speed, temperature, ambient light, ambientsound, etc.), while retrieving, from memory, a second environmentalcondition that was previously detected by a second sensor. Rather thaninstruct the second sensor to go online or activate to detect theenvironmental condition, the sensor management component 120 canretrieve the previously detected value from memory to reduce resourceutilization. For example, the sensor management component can poll afirst sensor for data corresponding to the first field, while obtainingdata from memory of the computing device 104 that corresponds to thesecond field. The second data can be stored in the memory of thecomputing device 104 prior to the data processing system 102 requestingthe second data. The sensor management component 120 may not poll thesecond sensor 134 responsive to the request for data from the secondsensor received from the direct action API 116. The sensor managementcomponent 120 can determine to poll the first sensor for datacorresponding to the first field, but not to poll the second sensor fordata corresponding to the second field. The sensor management component120 can use a policy, logic, of set of rules to determine whether or notto poll one or more sensors. For example, the policy, logic, or set ofrules can include, or be based, on conditional rules, if/thenconditions, trigger events, tolerances, thresholds, time interval,location, geographical fencing, or type of activity. For example, thesensor management component 120 can determine to poll the first sensorfor location information because the last location data received by thedata processing system 102 may have expired based on a time interval(e.g., 10 seconds, 5 seconds, 20 seconds, 30 seconds, 1 minute or more).The data processing system 102 can obtain data from memory for thesecond sensor because the second sensor may be a temperature sensor andthe data processing system 102 may determine that the timestamp of whenthe last temperature measurement was detected and stored in memory maysatisfy a time interval (e.g., 1 minute, 2 minutes, 3 minutes, 5minutes, 10 minutes, 20 minutes, 30 minutes or more).

The sensor management component 120 can adjust a configuration of asensor to collect data based on a type of data. For example, the sensormanagement component 120 can adjust a sample rate, a sample interval, ora sample duration for the sensor. The sensor management component 120can increase or decrease the sample rate. The sample rate can refer tothe number of samples taken during a measurement time interval. Thesample rate can include, for example, 0.005 Hz, 0.01 Hz, 0.015 Hz, 0.02Hz, 0.05 Hz, 0.1 Hz, 0.2 Hz, 0.5 Hz, 0.7 Hz, 1 Hz, 2 Hz, 3 Hz, 5 Hz, 10Hz or some other sample rate that provides data to create the actiondata structure while optimizing sensor resource utilization (e.g., datais within a tolerance level to perform an operation without excessivedata collection).

The sensor management component 120 can adjust a sample interval. Thesample interval can refer to a time period for when the sensor is turnedon or actively collecting sensor data. The sample duration can refer tohow long the sensor is turned on to actively collect data at the samplerate. For example, the sensor management component 120 can instruct orcommand the sensor to turn on every 5 minutes (e.g., sample interval) tocollect sensor data at 1 Hz (e.g., sample rate) for a duration of 30seconds (e.g., sample duration). The policies stored in policy datastructure 128 can include different values for the sample rate, sampleinterval, or duration based on the type of sensor, type of activity, orother characteristics.

In some cases, the sensor management component 120 can disable one ormore sensors. The sensor management component 120 can disable a sensorbased on or responsive to a characteristic of data collected by thesensor. The sensor management component 120 can temporarily orpermanently disable the sensor. Disabling the sensor 134 can prevent thesensor from collected or detecting sensor data or taking othermeasurements. In some cases, disabling the sensor can refer to disablingor turning off electronic hardware or an API that controls or interfaceswith the sensor. For example, the computing device 102 can stop pollingthe sensor for data and collecting and storing the data responsive to aninstruction from the sensor management component 120 to disable thesensor 134.

The sensor management component 120 can apply a resource utilizationreduction policy to a characteristic of data collected by the firstsensor to disable the first sensor. The characteristic of the data canbe include or refer to a quality of the data, quantity of the data, dataindicating a performance of the sensor, or data indicating availabilityof computing resource or battery power to continue collecting data. Forexample, the data can indicate that the sensor is malfunctioning, notcalibrated, or is otherwise collecting erroneous data. To preventcollecting further erroneous data and waste resources (e.g., batterypower, processing power, or bandwidth), the sensor management component120 can disable the sensor 134 for a time period. The sensor managementcomponent 120 can poll the sensor after the time period and assess thedata at that time. If the data again is unsatisfactory (e.g., notconsistent with data collected by other sensors, historical data, typeof data, format of the data), then the sensor management component 120can again disable the sensor for a time interval, which may be longerthan the first time interval or permanent until the sensor is repaired.

In some cases, data collected from a first sensor can be analyzed by thesensor management component 120 to disable a second sensor of the samecomputing device or another computing device. For example, a batterysensor can indicate that there is a low battery (e.g., 20% of batteryremaining), and then disable one or more sensors of the device 104. Inanother example, a temperature sensor can indicate that the device isoverheating, responsive to which the sensor management component 120 candisable a location sensor or other sensor.

In some cases, the data processing system can identify a plurality ofavailable sensors 134 configured to obtain a same or similar type ofinformation (e.g., location information, activity information, speed,temperature, ambient light, or ambient sound). The sensor managementcomponent 120 can determine that a sensor 134 is available if the sensor134 is functioning properly, can respond to requests for data, or canconvey detected data to a computing device 104 that is connected to thenetwork 104 to route the data to the data processing system 102. Thesensor management component 120 can determine that the sensor 134 isavailable if it is proximate to the end user of the computing device 104or otherwise in a location or configured to collect relevant data usedto generate the action data structure.

For example, the sensor management component 120 can determine thatthere are two sensors configured to collect location information thatare both associated with an account of an end user of the computingdevice 104. The sensor management component 120 can determine that bothsensors are proximate to the end user because they are both collectingsimilar location information based on a comparison of the data collectedby the two sensors. The sensor management component 120, applying apolicy, can determine to poll only one of the sensors for location dataand disable the other sensor in order to reduce resource consumption ascompared to having both sensors operating. For example, the two sensorsmay be configured on two different computing devices 104 that are bothproximate to the end user, such as a smartphone and a smartwatch. Thesensor management component 120 can determine a battery status of thetwo different computing devices 104, and select one of the two sensorsthat are configured on a computing device 104 with greater remainingbattery power. The battery power remaining can be a percentage ofremaining power, absolute power remaining, or an estimated amount oftime the battery can power the computing device 104 under currentutilization (e.g., processor or memory utilization, sensor utilization,network interface utilization). Thus, the sensor management component120 can disable one of the sensors to conserve battery consumption, andrequest the location information from the active sensor.

In some cases, the sensor management component 120 can piggy back off ofpreviously collected information. For example, the sensor managementcomponent can determine that there are a plurality of available sensorsproximate to the end user that are configured to obtain locationinformation. The plurality of available sensors can include a firstsensor, second sensor, or third sensor. The sensor management component120 can determine that the first sensor collected current locationinformation of the end user (e.g., within a threshold time such as 1second, 2 seconds, 3 seconds, or more). This information may have beencollected prior to the direct action API requesting sensor informationfrom the sensor management component 120. Further, the sensor managementcomponent 120 can determine that the third sensor is in an offline stateand lacks the current location information. Therefore, rather thancommand the third sensor to enter an online state and consume resources,the sensor management component 120 can determine to use the informationfrom the first sensor or poll the first sensor to collect updatedlocation information, while leaving the third sensor in the offline orlow-power state.

In some cases, the sensor management component 120 can instruct theclient device 104 to perform a batch upload of collected sensor data. Abatch upload of collected sensor data can reduce resource consumption ascompared to individual uploads or streaming data as the data iscollected by the sensor 134. The client device 104 can perform a batchupload responsive to entering an online state from an offline state(e.g., regaining connection to network 104; being turned on; or being inthe line-of-sight to a GPS satellite). In some cases, the sensormanagement component 120 can instruct the computing device 104 toperform a batch upload based on a location, such as entering or exitinga geofence (e.g., a retail location, or other physical location orarea). In some cases, the computing device can upload a list ofapplication installed on the computing device 104 (e.g., by accessing aregistry of the computing device or other database storing installedapplications).

The direct action API 116 can populate the one or more fields (e.g.,first field) with data received by the sensor management component 120responsive to the poll of the first sensor. The direct action API 116can populate the second field with the data received by the sensormanagement component 120 from memory of the client device to reduceresource consumption. The direct action API 116 can then generate anaction data structure based on the first field and the second field ofthe template.

The direct action API 116 can transmit the action data structure to athird party provider device (e.g., service provider device 108) to causethe third party provider device 108 to invoke an operation session. Anoperation session can refer to or include the third party providerdevice 108 conducting an operation with or based on the action datastructure, such as performing the requested service, purchasing therequested product, or invoking a conversational application programminginterface (e.g., service provider NLP component 142) to establish anoperation session or communication session between the third partyprovider device 108 and the client computing device 104. Responsive toestablishing the operation session or communication session between theservice provider device 108 and the client computing device 1004, theservice provider device 108 can transmit data packets directly to theclient computing device 104 via network 105. In some cases, the serviceprovider device 108 can transmit data packets to the client computingdevice 104 via data processing system 102 and network 105.

The data processing system 102 can receive, from the third partyprovider device 108, an indication that the third party provider device108 established the operation session with the client device 104. Forexample, the indication can identify the type of operation beingperformed (e.g., providing a service such as a taxi service; purchasinga product; responding to a query). The data processing system 102 canfurther receive an indication that an operation was performed during theoperation session (e.g., a taxi picked up the end user and transportedthe end user to the destination location). The indication that theoperation was performed can be provided via an operation data structurethat includes, for example, an identifier of the end user, timestamp,type of operation, identifier of third party service provider 108, orprice. The operation data structure can be formed using a template fromtemplate data structure 132. The template can be standard for alloperations, or be customized based on a type of operation.

In some cases, the third party provider device 108 can execute at leasta portion of the conversational API 142. For example, the third partyprovider device 108 can handle certain aspects of the communicationsession or types of queries. The third party provider device 108 mayleverage the NLP component 112 executed by the data processing system102 to facilitate processing the audio signals associated with thecommunication session and generating responses to queries. In somecases, the data processing system 102 can include the conversational API142 configured for the third party provider 108. In some cases, the dataprocessing system routes data packets between the client computingdevice and the third party provider device to establish thecommunication session. The data processing system 102 can receive, fromthe third party provider device 108, an indication that the third partyprovider device established the communication session with the clientdevice 104. The indication can include an identifier of the clientcomputing device 104, timestamp corresponding to when the communicationsession was established, or other information associated with thecommunication session, such as the action data structure associated withthe communication session. In some cases, the data processing system 102can include a session handler component 114 to manage an operationsession or communication session, and a sensor management component 120to manage or select sensors from which to collect data.

The data processing system 102 can include, execute, access, orotherwise communicate with a session handler component 114 to establisha communication session between the client device 104 and the dataprocessing system 102. The communication session can refer to one ormore data transmissions between the client device 104 and the dataprocessing system 102 that includes the input audio signal that isdetected by a sensor 134 of the client device 104, and the output signaltransmitted by the data processing system 102 to the client device 104.The data processing system 102 (e.g., via the session handler component114) can establish the communication session (e.g., operation session)responsive to receiving the input audio signal. The data processingsystem 102 can set a duration for the communication session. The dataprocessing system 102 can set a timer or a counter for the duration setfor the communication session. Responsive to expiration of the timer,the data processing system 102 can terminate the communication session.

The communication session can refer to a network-based communicationsession in which the client device 104 provides authenticatinginformation or credentials to establish the session. In some cases, thecommunication session refers to a topic or a context of audio signalscarried by data packets during the session. For example, a firstcommunication session can refer to audio signals transmitted between theclient device 104 and the data processing system 102 that are related to(e.g., include keywords, action data structures, or content itemobjects) a taxi service; and a second communication session can refer toaudio signals transmitted between the client device 104 and dataprocessing system 102 that are related to a laundry and dry cleaningservice. In this example, the data processing system 102 can determinethat the context of the audio signals are different (e.g., via the NLPcomponent 112), and separate the two sets of audio signals intodifferent communication sessions. The session handler 114 can terminatethe first session related to the ride service responsive to identifyingone or more audio signals related to the dry cleaning and laundryservice. Thus, the data processing system 102 can initiate or establishthe second session for the audio signals related to the dry cleaning andlaundry service responsive to detecting the context of the audiosignals.

The data processing system 102 can include, execute, or otherwisecommunicate with a content selector component 118 to receive the triggerkeyword identified by the natural language processor and select, basedon the trigger keyword, a content item via a real-time content selectionprocess. In some cases, the direct action API 116 can transmit theaction data structure to the content selector component 118 to performthe real-time content selection process and establish a communicationsession between the content provider device 106 (or a third partyprovider device 108) and the client computing device 104.

The content selection process can refer to, or include, selectingsponsored content item objects provided by third party content providers106. The content selection process can include a service in whichcontent items provided by multiple content providers are parsed,processed, weighted, or matched in order to select one or more contentitems to provide to the computing device 104. The content selectionprocess can be performed in real-time or offline. Performing the contentselection process in real-time can refer to performing the contentselection process responsive to the request for content received via theclient computing device 104. The real-time content selection process canbe performed (e.g., initiated or completed) within a time interval ofreceiving the request (e.g., 5 seconds, 10 seconds, 20 seconds, 30seconds, 1 minute, 2 minutes, 3 minutes, 5 minutes, 10 minutes, or 20minutes). The real-time content selection process can be performedduring a communication session with the client computing device 104, orwithin a time interval after the communication session is terminated.

For example, the data processing system 102 can include a contentselector component 118 designed, constructed, configured or operationalto select content item objects. To select content items for display in avoice-based environment, the data processing system 102 (e.g., via NLPcomponent 112) can parse the input audio signal to identify keywords(e.g., a trigger keyword), and use the keywords to select a matchingcontent item based on a broad match, exact match, or phrase match. Forexample, the content selector component 118 can analyze, parse, orotherwise process subject matter of candidate content items to determinewhether the subject matter of the candidate content items correspond tothe subject matter of the keywords or phrases of the input audio signaldetected by the microphone of the client computing device 104. Thecontent selector component 118 may identify, analyze, or recognizevoice, audio, terms, characters, text, symbols, or images of thecandidate content items using an image processing technique, characterrecognition technique, natural language processing technique, ordatabase lookup. The candidate content items may include metadataindicative of the subject matter of the candidate content items, inwhich case the content selector component 118 may process the metadatato determine whether the subject matter of the candidate content itemcorresponds to the input audio signal.

Content providers 106 may provide additional indicators when setting upa content campaign that includes content items. The content provider 106may provide information at the content campaign or content group levelthat the content selector component 118 may identify by performing alookup using information about the candidate content item. For example,the candidate content item may include a unique identifier, which maymap to a content group, content campaign, or content provider. Thecontent selector component 118 may determine, based on informationstored in content campaign data structure in data repository 124,information about the content provider 106.

The data processing system 102 can receive, via a computer network, arequest for content for presentation on a computing device 104. The dataprocessing system 102 can identify the request by processing an inputaudio signal detected by a microphone of the client computing device104. The request can include selection criteria of the request, such asthe device type, location, and a keyword associated with the request.The request can include the action data structure or action datastructure.

Responsive to the request, the data processing system 102 can select acontent item object from data repository 124 or a database associatedwith the content provider 106, and provide the content item forpresentation via the computing device 104 via network 105. The contentitem object can be provided by a content provider device 108 differentfrom the service provider device 108. The content item can correspond toa type of service different from a type of service of the action datastructure (e.g., taxi service versus food delivery service). Thecomputing device 104 can interact with the content item object. Thecomputing device 104 can receive an audio response to the content item.The computing device 104 can receive an indication to select a hyperlinkor other button associated with the content item object that causes orallows the computing device 104 to identify service provider 108,request a service from the service provider 108, instruct the serviceprovider 108 to perform a service, transmit information to the serviceprovider 108, or otherwise query the service provider device 108.

The data processing system 102 can include, execute, or communicate withan audio signal generator component 122 to generate an output signal.The output signal can include one or more portions. For example, theoutput signal can include a first portion and a second portion. Thefirst portion of the output signal can correspond to the action datastructure. The second portion of the output signal can correspond to thecontent item selected by the content selector component 118 during thereal-time content selection process.

The audio signal generator component 122 can generate the output signalwith a first portion having sound corresponding to the first datastructure. For example, the audio signal generator component 122 cangenerate the first portion of the output signal based on one or morevalues populated into the fields of the action data structure by thedirect action API 116. In a taxi service example, the values for thefields can include, for example, 123 Main Street for pick-up location,1234 Main Street for destination location, 2 for number of passengers,and economy for the level of service. The audio signal generatorcomponent 122 can generate the first portion of the output signal inorder to confirm that the end user of the computing device 104 wants toproceed with transmitting the request to the service provider 108. Thefirst portion can include the following output “Would you like to orderan economy car from taxi service provider A to pick two people up at 123Main Street and drop off at 1234 Main Street?”

In some cases, the first portion can include information received fromthe service provider device 108. The information received from serviceprovider device 108 can be customized or tailored for the action datastructure. For example, the data processing system 102 (e.g., via directaction API 116) can transmit the action data structure to the serviceprovider 108 before instructing the service provider 108 to perform theoperation. Instead, the data processing system 102 can instruct theservice provider device 108 to perform initial or preliminary processingon the action data structure to generate preliminary information aboutthe operation. In the example of the taxi service, the preliminaryprocessing on the action data structure can include identifyingavailable taxis that meet the level of service requirement that arelocated around the pick-up location, estimating an amount of time forthe nearest available taxi to reach the pick-up location, estimating atime of arrival at the destination, and estimating a price for the taxiservice. The estimated preliminary values may include a fixed value, anestimate that is subject to change based on various conditions, or arange of values. The service provider device 108 can return thepreliminary information to the data processing system 102 or directly tothe client computing device 104 via the network 104. The data processingsystem 102 can incorporate the preliminary results from the serviceprovider device 108 into the output signal, and transmit the outputsignal to the computing device 104. The output signal can include, forexample, “Taxi Service Company A can pick you up at 123 Main Street in10 minutes, and drop you off at 1234 Main Street by 9 AM for $10. Do youwant to order this ride?” This can form the first portion of the outputsignal.

In some cases, the data processing system 102 can form a second portionof the output signal. The second portion of the output signal caninclude a content item selected by the content selector component 118during a real-time content selection process. The first portion can bedifferent from the second portion. For example, the first portion caninclude information corresponding to the action data structure that isdirectly responsive to the data packets carrying the input audio signaldetected by the sensor 134 of the client computing device 104, whereasthe second portion can include a content item selected by a contentselector component 104 that can be tangentially relevant to the actiondata structure, or include sponsored content provided by a contentprovider device 106. For example, the end user of the computing device104 can request a taxi from Taxi Service Company A. The data processingsystem 102 can generate the first portion of the output signal toinclude information about the taxi from the Taxi Service Company A.However, the data processing system 102 can generate the second portionof the output signal to include a content item selected based on thekeywords “taxi service” and information contained in the action datastructure that the end user may be interested in. For example, thesecond portion can include a content item or information provided by adifferent taxi service company, such as Taxi Service Company B. Whilethe user may not have specifically requested Taxi Service Company B, thedata processing system 102 may nonetheless provide a content item fromTaxi Service Company B because the user may choose to perform anoperation with Taxi Service Company B.

The data processing system 102 can transmit information from the actiondata structure to the Taxi Service Company B to determine a pick-uptime, time of arrival at the destination, and a price for the ride. Thedata processing system 102 can receive this information and generate thesecond portion of the output signal as follows: “Taxi Service Company Bcan pick you up at 123 Main Street in 2 minutes, and drop you off at1234 Main Street by 8:52 AM for $15. Do you want this ride instead?” Theend user of computing device 104 can then select the ride provided byTaxi Service Company A or the ride provided by Taxi Service Company B.

Prior to providing, in the second portion of the output signal, thesponsored content item corresponding to the service provided by TaxiService Company B, the data processing system 102 can notify the enduser computing device that the second portion corresponds to a contentitem object selected during a real-time content selection process (e.g.,by the content selector component 118). However, the data processingsystem 102 can have limited access to different types of interfaces toprovide the notification to the end user of the computing device 104.For example, the computing device 104 may not include a display device,or the display device may be disabled or turned off. The display deviceof the computing device 104 may consume greater resources than thespeaker of the computing device 104, so it may be less efficient to turnon the display device of the computing device 104 as compared to usingthe speaker of the computing device 104 to convey the notification.Thus, in some cases, the data processing system 102 can improve theefficiency and effectiveness of information transmission over one ormore interfaces or one or more types of computer networks. For example,the data processing system 102 (e.g., via the audio signal generatorcomponent 122) can module the portion of the output audio signalcomprising the content item to provide the indication or notificationthe end user that that portion of the output signal comprises thesponsored content item.

The data processing system 102 (e.g., via interface 110 and network 105)can transmit data packets comprising the output signal generated by theaudio signal generator component 122. The output signal can cause theaudio driver component 138 of or executed by the client device 104 todrive a speaker (e.g., transducer 136) of the client device 104 togenerate an acoustic wave corresponding to the output signal.

FIG. 2 is an illustration of an operation of system to selectively pollsensors via a computer network. The system can include one or morecomponent of system 100 depicted in FIG. 1. The NLP component 112 canreceive and parse audio signals detected by a computing device. The NLPcomponent 112 can pass information to the direct action API 116 at ACT202. The direct action API 116 can determine to collect sensorinformation to populate one or more fields of a template to generate anaction data structure responsive to the audio signals parsed by the NLPcomponent 112.

At ACT 204, the direct action API can request the sensor informationfrom the sensor management component 120. The sensor managementcomponent 120 can establish a sensor data collection session 206 with aplurality of computing devices (e.g., first, second and third computingdevices 104). The sensor management component 120 can poll one or moreof the computing devices 104 at ACT 206. The sensor management component120 can determine to poll only one of the computing devices 104 forsensor data to reduce aggregate resource consumption among the pluralityof computing devices 104 that are related to the end user or thatprovide similar information. The sensor management component 120 caninstruct one or more of the computing devices 104 to poll sensors 134.

At ACT 208, one or more of the computing devices 104 can poll, enable,activate, invoke, or otherwise cause a sensor 134 to collect sensor dataand provide the sensor data to the computing device 104 to provide tothe sensor management component 120 or data processing system 102. Thesensor management component 120 can provide the collected sensor data tothe direct action API 116 at ACT 210. Thus, the sensor managementcomponent 120 can optimize sensor utilization to reduce aggregateresource utilization among the plurality of computing devices 104associated with the end user (or proximate to the end user that canprovide the requested sensor information) by selecting one or moresensors or a subset of sensors to poll.

FIG. 3 is an illustration of an example method of selectively pollingsensors. The method 300 can be performed by one or more component,system or element of system 100 or system 400. The method 300 caninclude a data processing system receiving an input audio signal (ACT305). The data processing system can receive the input audio signal froma client computing device. For example, a natural language processorcomponent executed by the data processing system can receive the inputaudio signal from a client computing device via an interface of the dataprocessing system. The data processing system can receive data packetsthat carry or include the input audio signal detected by a sensor of theclient computing device (or client device).

At ACT 310, the method 300 can include the data processing systemparsing the input audio signal. The natural language processor componentcan parse the input audio signal to identify a request and a triggerkeyword corresponding to the request. For example, the audio signaldetected by the client device can include “Okay device, I need a ridefrom Taxi Service Company A to go to 1234 Main Street.” In this audiosignal, the initial trigger keyword can include “okay device”, which canindicate to the client device to transmit an input audio signal to thedata processing system. A pre-processor of the client device can filterout the terms “okay device” prior to sending the remaining audio signalto the data processing system. In some cases, the client device canfilter out additional terms or generate keywords to transmit to the dataprocessing system for further processing.

The data processing system can identify a trigger keyword in the inputaudio signal. The trigger keyword can include, for example, “to go to”or “ride” or variations of these terms. The trigger keyword can indicatea type of service or product. The data processing system can identify arequest in the input audio signal. The request can be determined basedon the terms “I need”. The trigger keyword and request can be determinedusing a semantic processing technique or other natural languageprocessing technique.

At ACT 315, the data processing system can select a template for anaction data structure. The template can be responsive to the triggerkeyword, request, or identified third party provider. The template caninclude one or more fields, such as a first field.

At ACT 320, the data processing system can poll one or more sensors, orinstruct a computing device to poll a sensor. While the data processingsystem can identify multiple sensors capable of providing the data topopulate the first field, the data processing system can select onesensor to poll. The data processing system can determine to poll asensor that is not coupled to the computing device 104 that provided theinput audio signal. For example, an end user can use a first computingdevice 104 to provide the input audio signal. The first computing device104 can include a first sensor coupled to the first computing device.The data processing system can determine that there is a secondcomputing device 104 that is also associated with the end user account.The second computing device can be proximate to the first computingdevice, or the data processing system can otherwise determine that thesecond computing device can also provide the data of sufficient qualityto populate the first field of the template (e.g., the locationinformation that would be provided by the two sensors is within atolerance level, such as 25 meters, 50 meters, 75 meters, or 100meters). The data processing system can determine to use the secondsensor that is coupled to the second computing device 104 instead of thefirst sensor coupled to the first computing device, even though thefirst computing device invoked, initiated, or established thecommunication with the data processing system 102. The data processingsystem can use a resource reduction policy to determine to use thesecond sensor coupled to the second computing device. For example, thesecond computing device can have more battery remaining, have greaterresource availability, or be connected to power.

In some cases, the data processing system can determine to retrieve,from memory, data that was previously detected by a sensor. For example,the data processing system can determine to poll one sensor while notpolling a second sensor even though the information from the secondsensor is needed to populate the second field. For example, the dataprocessing system can determine to reduce resource consumption by usinginformation previously collected (e.g., prior to a request for sensordata). The data processing system can, therefore, poll the first sensorand use stored data from the second sensor. The data processing systemcan determine to do so based on rules or policies, or by analyzing thestored data to determine that it satisfies a condition or threshold(e.g., collected within a time interval).

At ACT 325, the data processing system can generate an action datastructure with the sensor data. The data processing system can generatethe action data structure based on the trigger keyword, request, thirdparty provider device, or other information. The action data structurecan be responsive to the request. For example, if the end user of theclient computing device requests a taxi from Taxi Service Company A, theaction data structure can include information to request a taxi servicefrom Taxi Service Company A. The data processing system can select atemplate for Taxi Service Company A, and populate fields in the templatewith values obtained from one or more sensors or memory to allow theTaxi Service Company A to send a taxi to the user of the clientcomputing device to pick up the user and transport the user to therequested destination.

At ACT 330, the data processing system can receive, from the third partyprovider device, an indication that the third party provider deviceestablished the operation session with the client device. The indicationcan indicate that the operation was initiated, is in pending, inprocess, or completed. The indication can include a data structure withadditional information about the operation.

FIG. 4 is a block diagram of an example computer system 400. Thecomputer system or computing device 400 can include or be used toimplement the system 100, or its components such as the data processingsystem 102. The data processing system 102 can include an intelligentpersonal assistant or voice-based digital assistant. The computingsystem 400 includes a bus 405 or other communication component forcommunicating information and a processor 410 or processing circuitcoupled to the bus 405 for processing information. The computing system400 can also include one or more processors 410 or processing circuitscoupled to the bus for processing information. The computing system 400also includes main memory 415, such as a random access memory (RAM) orother dynamic storage device, coupled to the bus 405 for storinginformation, and instructions to be executed by the processor 410. Themain memory 415 can be or include the data repository 145. The mainmemory 415 can also be used for storing position information, temporaryvariables, or other intermediate information during execution ofinstructions by the processor 410. The computing system 400 may furtherinclude a read only memory (ROM) 420 or other static storage devicecoupled to the bus 405 for storing static information and instructionsfor the processor 410. A storage device 425, such as a solid statedevice, magnetic disk or optical disk, can be coupled to the bus 405 topersistently store information and instructions. The storage device 425can include or be part of the data repository 145.

The computing system 400 may be coupled via the bus 405 to a display435, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 430, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 405 for communicating information and command selections to theprocessor 410. The input device 430 can include a touch screen display435. The input device 430 can also include a cursor control, such as amouse, a trackball, or cursor direction keys, for communicatingdirection information and command selections to the processor 410 andfor controlling cursor movement on the display 435. The display 435 canbe part of the data processing system 102, the client computing device150 or other component of FIG. 1, for example.

The processes, systems and methods described herein can be implementedby the computing system 400 in response to the processor 410 executingan arrangement of instructions contained in main memory 415. Suchinstructions can be read into main memory 415 from anothercomputer-readable medium, such as the storage device 425. Execution ofthe arrangement of instructions contained in main memory 415 causes thecomputing system 400 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory415. Hard-wired circuitry can be used in place of or in combination withsoftware instructions together with the systems and methods describedherein. Systems and methods described herein are not limited to anyspecific combination of hardware circuitry and software.

Although an example computing system has been described in FIG. 4, thesubject matter including the operations described in this specificationcan be implemented in other types of digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them.

For situations in which the systems discussed herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures that may collect personal information (e.g., information abouta user's social network, social actions or activities, a user'spreferences, or a user's location), or to control whether or how toreceive content from a content server or other data processing systemthat may be more relevant to the user. In addition, certain data may beanonymized in one or more ways before it is stored or used, so thatpersonally identifiable information is removed when generatingparameters. For example, a user's identity may be anonymized so that nopersonally identifiable information can be determined for the user, or auser's geographic location may be generalized where location informationis obtained (such as to a city, postal code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about him or her and usedby the content server.

The subject matter and the operations described in this specificationcan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. The subject matter described in thisspecification can be implemented as one or more computer programs, e.g.,one or more circuits of computer program instructions, encoded on one ormore computer storage media for execution by, or to control theoperation of, data processing apparatuses. Alternatively or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. A computer storage medium can be, or be includedin, a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination of one or more of them. While a computer storage medium isnot a propagated signal, a computer storage medium can be a source ordestination of computer program instructions encoded in an artificiallygenerated propagated signal. The computer storage medium can also be, orbe included in, one or more separate components or media (e.g., multipleCDs, disks, or other storage devices). The operations described in thisspecification can be implemented as operations performed by a dataprocessing apparatus on data stored on one or more computer-readablestorage devices or received from other sources.

The terms “data processing system” “computing device” “component” or“data processing apparatus” encompass various apparatuses, devices, andmachines for processing data, including by way of example a programmableprocessor, a computer, a system on a chip, or multiple ones, orcombinations of the foregoing. The apparatus can include special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). The apparatus can alsoinclude, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, a cross-platform runtime environment, avirtual machine, or a combination of one or more of them. The apparatusand execution environment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures. For example, the direct action API 116,content selector component 118, or NLP component 112 and other dataprocessing system 102 components can include or share one or more dataprocessing apparatuses, systems, computing devices, or processors.

A computer program (also known as a program, software, softwareapplication, app, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages,declarative or procedural languages, and can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program can correspond to a file in a filesystem. A computer program can be stored in a portion of a file thatholds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs (e.g., components of the data processing system 102)to perform actions by operating on input data and generating output. Theprocesses and logic flows can also be performed by, and apparatuses canalso be implemented as, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application specificintegrated circuit). Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

The subject matter described herein can be implemented in a computingsystem that includes a back end component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a front end component, e.g., a client computer having agraphical user interface or a web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or a combination of one or more such back end,middleware, or front end components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system such as system 100 or system 400 can includeclients and servers. A client and server are generally remote from eachother and typically interact through a communication network (e.g., thenetwork 165). The relationship of client and server arises by virtue ofcomputer programs running on the respective computers and having aclient-server relationship to each other. In some implementations, aserver transmits data (e.g., data packets representing a content item)to a client device (e.g., for purposes of displaying data to andreceiving user input from a user interacting with the client device).Data generated at the client device (e.g., a result of the userinteraction) can be received from the client device at the server (e.g.,received by the data processing system 102 from the computing device 150or the content provider computing device 155 or the service providercomputing device 160).

While operations are depicted in the drawings in a particular order,such operations are not required to be performed in the particular ordershown or in sequential order, and all illustrated operations are notrequired to be performed. Actions described herein can be performed in adifferent order.

The separation of various system components does not require separationin all implementations, and the described program components can beincluded in a single hardware or software product. For example, the NLPcomponent 110, the content selector component 125, or the sensormanagement component 120 can be a single component, app, or program, ora logic device having one or more processing circuits, or part of one ormore servers of the data processing system 102.

Having now described some illustrative implementations, it is apparentthat the foregoing is illustrative and not limiting, having beenpresented by way of example. In particular, although many of theexamples presented herein involve specific combinations of method actsor system elements, those acts and those elements may be combined inother ways to accomplish the same objectives. Acts, elements andfeatures discussed in connection with one implementation are notintended to be excluded from a similar role in other implementations orimplementations.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including” “comprising” “having” “containing” “involving”“characterized by” “characterized in that” and variations thereofherein, is meant to encompass the items listed thereafter, equivalentsthereof, and additional items, as well as alternate implementationsconsisting of the items listed thereafter exclusively. In oneimplementation, the systems and methods described herein consist of one,each combination of more than one, or all of the described elements,acts, or components.

Any references to implementations or elements or acts of the systems andmethods herein referred to in the singular may also embraceimplementations including a plurality of these elements, and anyreferences in plural to any implementation or element or act herein mayalso embrace implementations including only a single element. Referencesin the singular or plural form are not intended to limit the presentlydisclosed systems or methods, their components, acts, or elements tosingle or plural configurations. References to any act or element beingbased on any information, act or element may include implementationswhere the act or element is based at least in part on any information,act, or element.

Any implementation disclosed herein may be combined with any otherimplementation or embodiment, and references to “an implementation,”“some implementations,” “one implementation” or the like are notnecessarily mutually exclusive and are intended to indicate that aparticular feature, structure, or characteristic described in connectionwith the implementation may be included in at least one implementationor embodiment. Such terms as used herein are not necessarily allreferring to the same implementation. Any implementation may be combinedwith any other implementation, inclusively or exclusively, in any mannerconsistent with the aspects and implementations disclosed herein.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms. For example, a reference to “at least one of‘A’ and ‘B’” can include only ‘A’, only ‘B’, as well as both ‘A’ and‘B’. Such references used in conjunction with “comprising” or other openterminology can include additional items.

Where technical features in the drawings, detailed description or anyclaim are followed by reference signs, the reference signs have beenincluded to increase the intelligibility of the drawings, detaileddescription, and claims. Accordingly, neither the reference signs northeir absence have any limiting effect on the scope of any claimelements.

The systems and methods described herein may be embodied in otherspecific forms without departing from the characteristics thereof. Forexample, the data processing system 102 can select a content item for asubsequent action (e.g., for the third action 215) based in part on datafrom a prior action in the sequence of actions of the thread 200, suchas data from the second action 210 indicating that the second action 210is complete or about to begin. The foregoing implementations areillustrative rather than limiting of the described systems and methods.Scope of the systems and methods described herein is thus indicated bythe appended claims, rather than the foregoing description, and changesthat come within the meaning and range of equivalency of the claims areembraced therein.

What is claimed is:
 1. A system to selectively poll sensors via acomputer network, comprising: a natural language processor componentexecuted by a data processing system to receive, via an interface of thedata processing system, data packets comprising an input audio signaldetected by a microphone of a client device; the natural languageprocessor component to parse the input audio signal to identify arequest and a trigger keyword corresponding to the request; a directaction application programming interface (“API”) of the data processingsystem to select, based on the trigger keyword, a template for an actiondata structure responsive to the request, the action data structure topackage one or more parameters used by a third party provider device toperform an operation to provide a type of service or a type of product,the template comprising a first field; a sensor management component ofthe data processing system to: determine not to use a sensor of theclient device responsive based on at least one of the sensor in anoffline state, failure of the sensor to respond to a ping within a timeperiod, a malfunction of the sensor, or failure by the sensor to pass adiagnostic test; identify, responsive to the determination to not usethe sensor of the client device, a plurality of available sensors notcoupled to the client device that are coupled to a plurality of clientdevices that i) are each associated with an end user account associatedwith the client device, ii) are each within a threshold distance of theclient device, and iii) have each successfully performed a handshakingprocess with the data processing system using credentials of the enduser account and are currently online, the plurality of client devicescomprising a second client device and a third client device; identify aplurality of available sensors configured to obtain information for thefirst field of the action data structure used by the third partyprovider device to perform the operation to provide the type of serviceor the type of product, the plurality of available sensors comprising afirst sensor of the second client device and a second sensor of thethird client device; determine a status of each of the plurality ofavailable sensors; select, based on the status and the determination tonot use the sensor of the client device, the first sensor of theplurality of available sensors of the second client device of theplurality of client devices; poll the first sensor for datacorresponding to the first field of the action data structure used bythe third party provider device to perform the operation to provide thetype of service or the type of product; the direct action API topopulate the first field with the data received by the sensor managementcomponent responsive to the poll of the first sensor, and to generatethe action data structure to provide the type of service or the type ofproduct based on the first field of the template; the direct action APIto transmit the action data structure to the third party provider deviceto cause the third party provider device to invoke an operation sessionbetween the third party provider device and the client device andperform one or more actions that provide the type of service or the typeof product based on the one or more parameters packaged in the actiondata structure generated by the direct action API; and the dataprocessing system to receive, from the third party provider device, anindication that the third party provider device established theoperation session with the client device.
 2. The system of claim 1,comprising the data processing system to: receive an indication that theoperation was performed during the operation session.
 3. The system ofclaim 1, comprising the data processing system to: adjust aconfiguration of the first sensor to collect data based on a type ofdata.
 4. The system of claim 3, wherein the configuration comprises atleast one of a sample rate and sample interval.
 5. The system of claim1, comprising the data processing system to: determine the client deviceentered an online state; and command the client device to perform abatch upload of collected data responsive to the online state.
 6. Thesystem of claim 1, comprising the data processing system to: disable,responsive to a characteristic of data collected by the first sensor,the first sensor to prevent the first sensor from data collection. 7.The system of claim 1, comprising the data processing system to: apply aresource utilization reduction policy to a characteristic of datacollected by the first sensor to disable the first sensor.
 8. The systemof claim 1, comprising the data processing system to: identify a secondplurality of available sensors configured to obtain locationinformation, the second plurality of available sensors comprising thefirst sensor, the second sensor and a third sensor; determine a batterystatus of each of the plurality of available sensors; select the firstsensor of the plurality of available sensors based on the first sensorhaving greater battery power than the second sensor and the thirdsensor; disable the third sensor to conserve battery consumption; andrequest the location information from the first sensor.
 9. The system ofclaim 1, comprising the data processing system to: identify a secondplurality of available sensors configured to obtain locationinformation, the second plurality of available sensors comprising thefirst sensor and a third sensor; determine that the first sensordetected current location information of the client device prior to arequest for location information from the data processing system;determine that the third sensor is in an offline state and lacks thecurrent location information; and obtain the current locationinformation from the first sensor, wherein the third sensor is in theoffline state.
 10. The system of claim 1, comprising the data processingsystem to: receive, from the client device, a list of applicationsinstalled on the client device.
 11. A method of selectively pollingsensors via a computer network, comprising: receiving, by a naturallanguage processor component executed by a data processing system, viaan interface of the data processing system, data packets comprising aninput audio signal detected by a microphone of a client device; parsing,by the natural language processor component, the input audio signal toidentify a request and a trigger keyword corresponding to the request;selecting, by a direct action application programming interface (“API”)of the data processing system, based on the trigger keyword, a templatefor an action data structure responsive to the request, the action datastructure packaging one or more parameters used by a third partyprovider device to perform an operation to provide a type of service ora type of product, the template comprising a first field; determining,by a sensor management component of the data processing system, not touse a sensor of the client device responsive based on at least one ofthe sensor being in an offline state, failure of the sensor to respondto a ping within a time period, a malfunction of the sensor, or failureby the sensor to pass a diagnostic test; identifying, by the sensormanagement component of the data processing system responsive to thedetermination to not use the sensor of the client device, a plurality ofavailable sensors not coupled to the client device that are coupled to,a plurality of client devices that i) are each associated with an enduser account associated with the client device, ii) are each within athreshold distance of the client device, and iii) have each successfullyperformed a handshaking process with the data processing system usingcredentials of the end user account and are currently online, theplurality of client devices comprising a second client device and athird client device; identifying, by the sensor management component, aplurality of available sensors configured to obtain information for thefirst field of the action data structure used by the third partyprovider device to perform the operation to provide the type of serviceor the type of product, the plurality of available sensors comprising afirst sensor of the second client device and a second sensor of thethird client device; determining, by the sensor management component, astatus of each of the plurality of available sensors; selecting, by thesensor management component based on the status and the determination tonot use the sensor of the client device, the first sensor of theplurality of available sensors of the second client device of theplurality of client devices; polling, by the sensor managementcomponent, the first sensor for data corresponding to the first field ofthe action data structure used by the third party provider device toperform the operation to provide the type of service or the type ofproduct; populating, by the direct action API, the first field based onthe data received by the sensor management component responsive to thepoll of the first sensor; generating, by the direct action API, theaction data structure to provide the type of service or the type ofproduct based on the first field of the template; and transmitting, bythe direct action API, the action data structure to the third partyprovider device to cause the third party provider device to invoke anoperation session between the third party provider device and the clientdevice and perform one or more actions that provide the type of serviceor the type of product based on the one or more parameters packaged inthe action data structure generated by the direct action API; andreceiving, by the data processing system, from the third party providerdevice, an indication that the third party provider device establishedthe operation session with the client device.
 12. The method of claim11, comprising: receiving, by the data processing system, an indicationthat the operation was performed during the operation session.
 13. Themethod of claim 11, comprising: adjusting, by the data processingsystem, a configuration of the first sensor to collect data based on atype of data.
 14. The method of claim 11, wherein the configurationcomprising at least one of a sample rate and sample interval.
 15. Themethod of claim 11, comprising: determining, by the data processingsystem, the client device entered an online state; and commanding, bythe data processing system, the client device to perform a batch uploadof collected data responsive to the online state.
 16. The method ofclaim 11, comprising: disabling, by the data processing system,responsive to a characteristic of data collected by the first sensor,the first sensor to prevent the first sensor from data collection. 17.The method of claim 11, comprising: applying, by the data processingsystem, a resource utilization reduction policy to a characteristic ofdata collected by the first sensor to disable the first sensor.
 18. Themethod of claim 11, comprising: identifying, by the data processingsystem, a second plurality of available sensors configured to obtainlocation information, the second plurality of available sensorscomprising the first sensor, the second sensor and a third sensor;determining a battery status of each of the second plurality ofavailable sensors; selecting the first sensor of the second plurality ofavailable sensors based on the first sensor having greater battery powerthan the second sensor and the third sensor; disabling the third sensorto conserve battery consumption; and requesting the location informationfrom the first sensor.
 19. The method of claim 11, comprising:identifying a second plurality of available sensors configured to obtainlocation information, the second plurality of available sensorscomprising the first sensor and a third sensor; determining that thefirst sensor detected current location information of the client deviceprior to a request for location information from the data processingsystem; determining that the third sensor is in an offline state andlacks the current location information; and obtaining the currentlocation information from the first sensor, the third sensor in theoffline state.
 20. The method of claim 11, comprising: receiving, fromthe client device, a list of applications installed on the clientdevice.